Joint Neural Network for Dialogue State Tracking
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Solution Overview
Problem
Current task-oriented dialogue systems face challenges in efficiently handling large or unbounded sets of slot values and dealing with values not seen during training, while also bridging the gap between training and inference scenarios, due to separate neural networks for language understanding and dialogue state tracking and reliance on ground truth slot values and dialogue states.
Innovation Solution
The proposed solution involves a multi-task learning approach that shares neural network layers for encoding user utterances for both language understanding and dialogue state tracking, using scheduled sampling to adapt outputs for inference, and employing a slot tagger and candidate scorer to handle diverse slot values, thereby reducing network parameters and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural networks are used for language understanding and dialogue state tracking, then each task can be optimized independently, but the number of network parameters increases and computational efficiency decreases
Solution Approach 1:
The patent merges language understanding and dialogue state tracking into a single joint neural network model. The shared encoder processes user utterances and generates representations that are simultaneously used for both LU intent classification and DST slot value prediction, eliminating redundancy and reducing total parameters while maintaining task-specific performance through separate output heads.
Solution Approach 2:
The joint model employs a universal encoder that performs multiple functions: encoding user utterances for intent classification, extracting slot values, and maintaining dialogue state. This multi-functional component replaces separate specialized networks, achieving parameter efficiency without sacrificing task-specific optimization through shared feature representations.
2Measurement precision
If ground truth slot values and dialogue states are used during training, then training accuracy is improved, but the model cannot handle out-of-vocabulary slot values and values not seen during training
Solution Approach 1:
The patent implements dynamic candidate set generation that adapts to unseen slot values during inference. Instead of relying on fixed training vocabulary, the system dynamically generates candidate slot values from the dialogue context and user utterance, allowing the model to handle out-of-vocabulary values while maintaining training accuracy through scheduled sampling that gradually transitions from ground truth to dynamic candidates.
Solution Approach 2:
The patent introduces an intermediary candidate generation module that bridges training and inference scenarios. This module generates candidate slot values from dialogue context and utterance encodings, serving as a mediator between the fixed training data and the open-ended inference scenarios with unseen values, enabling the model to generalize to new vocabulary while maintaining training performance.
3Productivity
If a joint model is used for language understanding and dialogue state tracking, then the number of network parameters is reduced and computational efficiency is improved, but handling large or unbounded sets of slot values becomes more challenging
Solution Approach 1:
The patent segments the slot value handling process into distinct components: candidate generation from dialogue context, candidate scoring using the joint model, and selection of final slot values. This segmentation allows the efficient joint model to focus on scoring a manageable candidate set rather than directly processing large unbounded value spaces, maintaining computational efficiency while handling diverse slot values.
Solution Approach 2:
The patent applies partial action by generating and scoring only a subset of relevant candidate slot values rather than evaluating all possible values. The candidate generation module filters the large value space to relevant candidates based on dialogue context, allowing the joint model to efficiently process a reduced set while still handling the full range of possible slot values through selective evaluation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for dialogue systems. A transcription of a user utterance is obtained. The transcription of the utterance is tokenized to identify multiple tokens for the utterance. Token-level utterance encodings corresponding to different tokens of the transcription are generated. A system action encoding from data indicating system actions previously performed by the dialogue system are generated. A dialogue context vector based on the utterance encoding and the system action encoding are generated. The token-level utterance encodings, the system action encoding, and the dialogue context vector are processed using a slot tagger to produce token-level output vectors. A limited set of candidate token classifications for the tokens of the user utterance are determined based on the token-level utterance encodings. A response for output is provided in response to the user utterance.